A pattern recognition system can be an Optical Character Recognition (OCR). OCR systems are known. They convert the image of text into machine-readable code by using a character recognition process. In an OCR system, the images of what could be characters are isolated and a character recognition process is used to identify the character.
Known optical character recognition processes generally comprise:                a normalization step that generates a normalized matrix from an input image;        a feature extraction step; and        a classification step to identify the character.        
In some OCR processes, the feature extraction step involves a matrix vector multiplication between a matrix representing a filter and a vector representing the input image.
Several methods are known for matrix vector multiplications. However, known methods are slow because of the numerous number of calculation steps required.
WO2007/095516 A3 is directed to the problem of accelerating sparse matrix computation. A system and a method are disclosed to decrease the memory traffic required by operations involving a sparse matrix, like a matrix vector multiplication, because the memory bandwidth is a bottleneck in such operations and these system and method concern the storage of, and the access to, a sparse matrix on the memory of a microprocessor.
Another known method of determining a matrix multiplication route for an at least approximate multiplication by a coefficient matrix F is disclosed in WO2004/042601 A2. This method writes the coefficient matrix F as the product of a quantised coefficient matrix G and a correction factor matrix H.